COVID-19 and Travel: How Our Out-of-home Travel Activity, In-home Activity, and Long-Distance Travel Have Changed
Bibliographic record
Abstract
COVID-19 has made unprecedented impacts on our daily life. This paper investigates individuals' immediate response to COVID-19, exploring out-of-home activities, in-home activities, and long-distance travel. Data for the Kelowna region of Canada comes from a web-based COVID-19 Survey for assessing Travel impact (COST). In addition to analyzing the survey, this research models adjustments in travel decisions by developing ordered logit models for in-home and out-of-home activities, and a binomial logit model for long-distance travel. Data analysis suggests a reduction of about 50% out-of-home activities/day/person during COVID-19 compared to the pre-pandemic period, with the only exception being picking up online orders which significantly increased in frequency. Individuals were engaged in longer duration of in-home activities; the average duration of teleworking, online shopping for groceries and other goods at-home was around 5.5 h/day/person, 32 min/day/person, and 26 min/day/person respectively. The out-of-home activity model results suggest that higher income, younger and middle aged individuals, and full-time workers are more likely to decrease their out-of-home activity; whereas, males, lower income groups, health care professionals, and picking up online orders are more likely to increase. The in-home activity model suggests that older and younger adults, higher and lower income, full-time workers, and highly educated individuals are most likely to increase their in-home activity frequency; in contrast, health care professionals are likely to decrease. Long-distance travel model results reveal that seniors, students, and airline travelers are more likely to reschedule; whereas, trips to visit friends and family are more likely to be cancelled.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".